Industry increasingly depends upon highly automated data acquisition and control systems to ensure that industrial processes are run efficiently, safely and reliably while lowering their overall production costs. Data acquisition begins when a number of sensors measure aspects of an industrial process and periodically report their measurements back to a data collection and control system. Such measurements come in a wide variety of forms. By way of example, the measurements produced by a sensor/recorder include: a temperature, a pressure, a pH, a mass/volume flow of material, a tallied inventory of packages waiting in a shipping line, or a photograph of a room in a factory. Often sophisticated process management and control software examines the incoming data, produces status reports, and, in many cases, responds by sending commands to actuators/controllers that adjust the operation of at least a portion of the industrial process. The data produced by the sensors also allow an operator to perform a number of supervisory tasks including: tailor the process (e.g., specify new set points) in response to varying external conditions (including costs of raw materials), detect an inefficient/non-optimal operating condition and/or impending equipment failure, and take remedial actions such as move equipment into and out of service as required.
Typical industrial processes are extremely complex and receive substantially greater volumes of information than any human could possibly digest in its raw form. By way of example, it is not unheard of to have thousands of sensors and control elements (e.g., valve actuators) monitoring/controlling aspects of a multi-stage process within an industrial plant. These sensors are of varied type and report on varied characteristics of the process. Their outputs are similarly varied in the meaning of their measurements, in the amount of data sent for each measurement, and in the frequency of their measurements. As regards the latter, for accuracy and to enable quick response, some of these sensors/control elements take one or more measurements every second. Multiplying a single sensor/control element by thousands of sensors/control elements (a typical industrial control environment) results in an overwhelming volume of data flowing into the manufacturing information and process control system. Sophisticated data management and process visualization techniques have been developed to handle the large volumes of data generated by such system.
Highly advanced human-machine interface/process visualization systems exist today that are linked to data sources such as the above-described sensors and controllers. Such systems acquire and digest (e.g., filter) the process data described above. The digested process data in-turn drives a graphical display rendered by a human machine interface. Examples of such systems are the well-known Wonderware INTOUCH® human-machine interface (HMI) software system for visualizing and controlling a wide variety of industrial processes and the ArchestrA™ (e.g., the application server or AppServer for INTOUCH™) comprehensive automation and information software open architecture designed to integrate and extend the life of legacy systems by leveraging the latest, open industry standards and software technologies.
At these industrial sites, personnel may also perform local inspections, maintenance or repair of equipment and sensor systems for such equipment in the field. Personnel can access information from a data and control acquisition system regarding the equipment, sensor systems, and their operational status; however, it may be difficult to associate such data with a specific piece of equipment on the site. It may also be difficult for personnel to physically access specific equipment and their sensor systems at the site, and thus, to locally access information from the sensor systems.